






from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TFRoFormerForCausalLM,
      TFRoFormerForMaskedLM,
      TFRoFormerForMultipleChoice,
      TFRoFormerForQuestionAnswering,
      TFRoFormerForSequenceClassification,
      TFRoFormerForTokenClassification,
      TFRoFormerModel,
  )
  from transformers.models.roformer.modeling_tf_roformer import (
      TFRoFormerSelfAttention,
      TFRoFormerSinusoidalPositionalEmbedding,
  )
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Union[str, Any]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Union[str, Any]=13       ,				lowerCamelCase		:			str=7       ,				lowerCamelCase		:			Tuple=True       ,				lowerCamelCase		:			Union[str, Any]=True       ,				lowerCamelCase		:			List[Any]=True       ,				lowerCamelCase		:			int=True       ,				lowerCamelCase		:			Optional[int]=99       ,				lowerCamelCase		:			Any=32       ,				lowerCamelCase		:			Union[str, Any]=2       ,				lowerCamelCase		:			Optional[Any]=4       ,				lowerCamelCase		:			Union[str, Any]=37       ,				lowerCamelCase		:			Dict="gelu"       ,				lowerCamelCase		:			Dict=0.1       ,				lowerCamelCase		:			List[Any]=0.1       ,				lowerCamelCase		:			List[Any]=512       ,				lowerCamelCase		:			Any=16       ,				lowerCamelCase		:			str=2       ,				lowerCamelCase		:			Optional[Any]=0.02       ,				lowerCamelCase		:			int=3       ,				lowerCamelCase		:			Optional[int]=4       ,				lowerCamelCase		:			List[Any]=None       ,				)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    13
         _UpperCAmelCase							=    7
         _UpperCAmelCase							=    True
         _UpperCAmelCase							=    True
         _UpperCAmelCase							=    True
         _UpperCAmelCase							=    True
         _UpperCAmelCase							=    99
         _UpperCAmelCase							=    32
         _UpperCAmelCase							=    2
         _UpperCAmelCase							=    4
         _UpperCAmelCase							=    37
         _UpperCAmelCase							=    """gelu"""
         _UpperCAmelCase							=    0.1
         _UpperCAmelCase							=    0.1
         _UpperCAmelCase							=    512
         _UpperCAmelCase							=    16
         _UpperCAmelCase							=    2
         _UpperCAmelCase							=    0.02
         _UpperCAmelCase							=    3
         _UpperCAmelCase							=    4
         _UpperCAmelCase							=    None


  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size							)

         _UpperCAmelCase							=    None
         if self.use_input_mask:
                _UpperCAmelCase							=    random_attention_mask([self.batch_size, self.seq_length]							)

         _UpperCAmelCase							=    None
         if self.use_token_type_ids:
                _UpperCAmelCase							=    ids_tensor([self.batch_size, self.seq_length]       ,				self.type_vocab_size							)

         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    None
         if self.use_labels:
                _UpperCAmelCase							=    ids_tensor([self.batch_size]       ,				self.type_sequence_label_size							)
                _UpperCAmelCase							=    ids_tensor([self.batch_size, self.seq_length]       ,				self.num_labels							)
                _UpperCAmelCase							=    ids_tensor([self.batch_size]       ,				self.num_choices							)

         _UpperCAmelCase							=    RoFormerConfig(
             vocab_size=self.vocab_size       ,				hidden_size=self.hidden_size       ,				num_hidden_layers=self.num_hidden_layers       ,				num_attention_heads=self.num_attention_heads       ,				intermediate_size=self.intermediate_size       ,				hidden_act=self.hidden_act       ,				hidden_dropout_prob=self.hidden_dropout_prob       ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,				max_position_embeddings=self.max_position_embeddings       ,				type_vocab_size=self.type_vocab_size       ,				initializer_range=self.initializer_range       ,				return_dict=lowerCamelCase       ,				)

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Optional[int]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    TFRoFormerModel(config=lowerCamelCase							)
         _UpperCAmelCase							=    {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

         _UpperCAmelCase							=    [input_ids, input_mask]
         _UpperCAmelCase							=    model(lowerCamelCase							)

         _UpperCAmelCase							=    model(lowerCamelCase							)

         self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)							)


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Optional[Any]							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    True
         _UpperCAmelCase							=    TFRoFormerForCausalLM(config=lowerCamelCase							)
         _UpperCAmelCase							=    {
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }
         _UpperCAmelCase							=    model(lowerCamelCase							)["""logits"""]
         self.parent.assertListEqual(
             list(prediction_scores.numpy().shape							)       ,				[self.batch_size, self.seq_length, self.vocab_size]							)


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			List[Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    TFRoFormerForMaskedLM(config=lowerCamelCase							)
         _UpperCAmelCase							=    {
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }
         _UpperCAmelCase							=    model(lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length, self.vocab_size)							)


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			int							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.num_labels
         _UpperCAmelCase							=    TFRoFormerForSequenceClassification(config=lowerCamelCase							)
         _UpperCAmelCase							=    {
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }

         _UpperCAmelCase							=    model(lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)							)


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			List[Any]							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    self.num_choices
         _UpperCAmelCase							=    TFRoFormerForMultipleChoice(config=lowerCamelCase							)
         _UpperCAmelCase							=    tf.tile(tf.expand_dims(lowerCamelCase       ,				1							)       ,				(1, self.num_choices, 1)							)
         _UpperCAmelCase							=    tf.tile(tf.expand_dims(lowerCamelCase       ,				1							)       ,				(1, self.num_choices, 1)							)
         _UpperCAmelCase							=    tf.tile(tf.expand_dims(lowerCamelCase       ,				1							)       ,				(1, self.num_choices, 1)							)
         _UpperCAmelCase							=    {
             """input_ids""": multiple_choice_inputs_ids,
             """attention_mask""": multiple_choice_input_mask,
             """token_type_ids""": multiple_choice_token_type_ids,
         }
         _UpperCAmelCase							=    model(lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_choices)							)


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			int							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    self.num_labels
         _UpperCAmelCase							=    TFRoFormerForTokenClassification(config=lowerCamelCase							)
         _UpperCAmelCase							=    {
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }
         _UpperCAmelCase							=    model(lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length, self.num_labels)							)


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Dict							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    TFRoFormerForQuestionAnswering(config=lowerCamelCase							)
         _UpperCAmelCase							=    {
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }

         _UpperCAmelCase							=    model(lowerCamelCase							)
         self.parent.assertEqual(result.start_logits.shape       ,				(self.batch_size, self.seq_length)							)
         self.parent.assertEqual(result.end_logits.shape       ,				(self.batch_size, self.seq_length)							)





  def  lowerCamelCase       (      self		:			Dict							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    self.prepare_config_and_inputs()
         (
             (
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			
         )							=    config_and_inputs
         _UpperCAmelCase							=    {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
         return config, inputs_dict



@require_tf
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (
      (
          TFRoFormerModel,
          TFRoFormerForCausalLM,
          TFRoFormerForMaskedLM,
          TFRoFormerForQuestionAnswering,
          TFRoFormerForSequenceClassification,
          TFRoFormerForTokenClassification,
          TFRoFormerForMultipleChoice,
      )
      if is_tf_available()
      else ()
  )
  _lowerCamelCase    =  (
      {
          '''feature-extraction''': TFRoFormerModel,
          '''fill-mask''': TFRoFormerForMaskedLM,
          '''question-answering''': TFRoFormerForQuestionAnswering,
          '''text-classification''': TFRoFormerForSequenceClassification,
          '''text-generation''': TFRoFormerForCausalLM,
          '''token-classification''': TFRoFormerForTokenClassification,
          '''zero-shot''': TFRoFormerForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )

  _lowerCamelCase    =  False
  _lowerCamelCase    =  False


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Optional[int]							)						->	Any:


         """simple docstring"""
         if pipeline_test_casse_name == "TextGenerationPipelineTests":
                return True

         return False


  def  lowerCamelCase       (      self		:			Any							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    TFRoFormerModelTester(self							)
         _UpperCAmelCase							=    ConfigTester(self       ,				config_class=lowerCamelCase       ,				hidden_size=37							)


  def  lowerCamelCase       (      self		:			List[str]							)						->	Any:


         """simple docstring"""
         self.config_tester.run_common_tests()


  def  lowerCamelCase       (      self		:			List[str]							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			List[Any]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_lm_head(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			str							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			List[str]							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			int							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowerCamelCase							)





  @slow
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""							)
         self.assertIsNotNone(lowerCamelCase							)



@require_tf
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  @slow
  def  lowerCamelCase       (      self		:			Dict							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""							)
         _UpperCAmelCase							=    tf.constant([[0, 1, 2, 3, 4, 5]]							)
         _UpperCAmelCase							=    model(lowerCamelCase							)[0]

         # TODO Replace vocab size
         _UpperCAmelCase							=    5_0000

         _UpperCAmelCase							=    [1, 6, vocab_size]
         self.assertEqual(output.shape       ,				lowerCamelCase							)

         print(output[:, :3, :3]							)

         # TODO Replace values below with what was printed above.
         _UpperCAmelCase							=    tf.constant(
             [
                 [
                     [-0.1205_3341, -1.026_4901, 0.2922_1946],
                     [-1.513_3783, 0.19_7433, 0.1519_0607],
                     [-5.013_5403, -3.90_0256, -0.8403_8764],
                 ]
             ]							)
         tf.debugging.assert_near(output[:, :3, :3]       ,				lowerCamelCase       ,				atol=1E-4							)



@require_tf
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  1E-4


  def  lowerCamelCase       (      self		:			Tuple							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    tf.constant([[4, 10]]							)
         _UpperCAmelCase							=    TFRoFormerSinusoidalPositionalEmbedding(num_positions=6       ,				embedding_dim=6							)

         _UpperCAmelCase							=    emba(input_ids.shape							)
         _UpperCAmelCase							=    tf.constant(
             [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]							)

         tf.debugging.assert_near(lowerCamelCase       ,				lowerCamelCase       ,				atol=self.tolerance							)





  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    tf.constant(
             [
                 [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
                 [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
                 [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
             ]							)
         _UpperCAmelCase							=    TFRoFormerSinusoidalPositionalEmbedding(num_positions=512       ,				embedding_dim=512							)
         emba([2, 16, 512]							)
         _UpperCAmelCase							=    emba.weight[:3, :5]

         tf.debugging.assert_near(lowerCamelCase       ,				lowerCamelCase       ,				atol=self.tolerance							)



@require_tf
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  1E-4


  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	int:


         """simple docstring"""
         # 2,12,16,64
         _UpperCAmelCase							=    tf.reshape(tf.range(2 * 12 * 16 * 64       ,				dtype=tf.floataa							)       ,				shape=(2, 12, 16, 64)							) / 100

         _UpperCAmelCase							=    -tf.reshape(tf.range(2 * 12 * 16 * 64       ,				dtype=tf.floataa							)       ,				shape=(2, 12, 16, 64)							) / 100

         _UpperCAmelCase							=    TFRoFormerSinusoidalPositionalEmbedding(num_positions=32       ,				embedding_dim=64							)
         _UpperCAmelCase							=    embed_positions([2, 16, 768]							)[None, None, :, :]

         _UpperCAmelCase	,			_UpperCAmelCase							=    TFRoFormerSelfAttention.apply_rotary_position_embeddings(
             lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							)

         _UpperCAmelCase							=    tf.constant(
             [
                 [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
                 [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
                 [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
                 [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
                 [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
                 [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
             ]							)
         _UpperCAmelCase							=    tf.constant(
             [
                 [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
                 [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
                 [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
                 [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
                 [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
                 [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
             ]							)

         tf.debugging.assert_near(query_layer[0, 0, :6, :8]       ,				lowerCamelCase       ,				atol=self.tolerance							)
         tf.debugging.assert_near(key_layer[0, 0, :6, :8]       ,				lowerCamelCase       ,				atol=self.tolerance							)

"""simple docstring"""


import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


SCREAMING_SNAKE_CASE			  =  logging.getLogger(__name__)
torch.set_grad_enabled(False)
SCREAMING_SNAKE_CASE			  =  """cuda""" if torch.cuda.is_available() else """cpu"""



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_=1_00		,   UpperCAmelCase_=" "					)->				List[str]:







       """simple docstring"""
       UpperCamelCase       			=		text.split(UpperCAmelCase_					)
       return [character.join(text[i : i + n]					).strip() for i in range(0		,   len(UpperCAmelCase_					)		,   UpperCAmelCase_					)]



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				dict:







       """simple docstring"""
       UpperCamelCase			,							UpperCamelCase       			=		[], []
       for title, text in zip(documents["title"]		,   documents["text"]					):
              if text is not None:
                     for passage in split_text(UpperCAmelCase_					):
                            titles.append(title if title is not None else ""					)
                            texts.append(UpperCAmelCase_					)
       return {"title": titles, "text": texts}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				dict:







       """simple docstring"""
       UpperCamelCase       			=		ctx_tokenizer(
           documents["title"]		,   documents["text"]		,   truncation=UpperCAmelCase_		,   padding="longest"		,   return_tensors="pt"					)["input_ids"]
       UpperCamelCase       			=		ctx_encoder(input_ids.to(device=UpperCAmelCase_					)		,   return_dict=UpperCAmelCase_					).pooler_output
       return {"embeddings": embeddings.detach().cpu().numpy()}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   )->				List[str]:







       """simple docstring"""
       ######################################
       logger.info("Step 1 - Create the dataset"					)
       ######################################

       # The dataset needed for RAG must have three columns:
       # - title (string): title of the document
       # - text (string): text of a passage of the document
       # - embeddings (array of dimension d): DPR representation of the passage
       # Let's say you have documents in tab-separated csv files with columns "title" and "text"
       assert os.path.isfile(rag_example_args.csv_path					), "Please provide a valid path to a csv file"

       # You can load a Dataset object this way
       UpperCamelCase       			=		load_dataset(
           "csv"		,   data_files=[rag_example_args.csv_path]		,   split="train"		,   delimiter="\t"		,   column_names=["title", "text"]					)

       # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

       # Then split the documents into passages of 100 words
       UpperCamelCase       			=		dataset.map(UpperCAmelCase_		,   batched=UpperCAmelCase_		,   num_proc=processing_args.num_proc					)

       # And compute the embeddings
       UpperCamelCase       			=		DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=UpperCAmelCase_					)
       UpperCamelCase       			=		DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
       UpperCamelCase       			=		Features(
           {"text": Value("string"					), "title": Value("string"					), "embeddings": Sequence(Value("float32"					)					)}					)  # optional, save as float32 instead of float64 to save space
       UpperCamelCase       			=		dataset.map(
           partial(UpperCAmelCase_		,   ctx_encoder=UpperCAmelCase_		,   ctx_tokenizer=UpperCAmelCase_					)		,   batched=UpperCAmelCase_		,   batch_size=processing_args.batch_size		,   features=UpperCAmelCase_		,   )

       # And finally save your dataset
       UpperCamelCase       			=		os.path.join(rag_example_args.output_dir		,   "my_knowledge_dataset"					)
       dataset.save_to_disk(UpperCAmelCase_					)
       # from datasets import load_from_disk
       # dataset = load_from_disk(passages_path)  # to reload the dataset

       ######################################
       logger.info("Step 2 - Index the dataset"					)
       ######################################

       # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
       UpperCamelCase       			=		faiss.IndexHNSWFlat(index_hnsw_args.d		,   index_hnsw_args.m		,   faiss.METRIC_INNER_PRODUCT					)
       dataset.add_faiss_index("embeddings"		,   custom_index=UpperCAmelCase_					)

       # And save the index
       UpperCamelCase       			=		os.path.join(rag_example_args.output_dir		,   "my_knowledge_dataset_hnsw_index.faiss"					)
       dataset.get_index("embeddings"					).save(UpperCAmelCase_					)
       # dataset.load_faiss_index("embeddings", index_path)  # to reload the index



@dataclass
class 	__a	:
       UpperCamelCase_		:       str				=      field(
           default=str(Path(_lowerCAmelCase						).parent / '''test_run''' / '''dummy-kb''' / '''my_knowledge_dataset.csv'''						)     ,  metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}     ,  )
       UpperCamelCase_		:       Optional[str]				=      field(
           default=_lowerCAmelCase     ,  metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default='''facebook/rag-sequence-nq'''     ,  metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default='''facebook/dpr-ctx_encoder-multiset-base'''     ,  metadata={
               '''help''': (
                   '''The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'''
                   ''' \'facebook/dpr-ctx_encoder-multiset-base\''''
               )
           }     ,  )
       UpperCamelCase_		:       Optional[str]				=      field(
           default=str(Path(_lowerCAmelCase						).parent / '''test_run''' / '''dummy-kb'''						)     ,  metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       Optional[int]				=      field(
           default=_lowerCAmelCase     ,  metadata={
               '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
           }     ,  )
       UpperCamelCase_		:       int				=      field(
           default=16     ,  metadata={
               '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
           }     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       int				=      field(
           default=768     ,  metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}     ,  )
       UpperCamelCase_		:       int				=      field(
           default=128     ,  metadata={
               '''help''': (
                   '''The number of bi-directional links created for every new element during the HNSW index construction.'''
               )
           }     ,  )


if __name__ == "__main__":
      logging.basicConfig(level=logging.WARNING)
      logger.setLevel(logging.INFO)

      SCREAMING_SNAKE_CASE			  =  HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
      SCREAMING_SNAKE_CASE  ,   SCREAMING_SNAKE_CASE  ,   SCREAMING_SNAKE_CASE			  =  parser.parse_args_into_dataclasses()
      with TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE			  =  rag_example_args.output_dir or tmp_dir
            main(rag_example_args, processing_args, index_hnsw_args)





